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Deep Learning Glossary

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Deep Learning Glossary

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1. Embedding

An Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandA Python-Based Deep Learning Library That Includes Many High-Level Building Blocks For Deep Neural Networks. It Can Run On Top Of Either Tensorflow Or TheanoSee Activation Function

2. Tensorflow

3. Nonlinearity

An Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasSee Activation FunctionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandA Python-Based Deep Learning Library That Includes Many High-Level Building Blocks For Deep Neural Networks. It Can Run On Top Of Either Tensorflow Or Theano

4. Keras

An Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasSee Activation FunctionA Python-Based Deep Learning Library That Includes Many High-Level Building Blocks For Deep Neural Networks. It Can Run On Top Of Either Tensorflow Or TheanoMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At Hand

5. Channel

Input Data To Deep Learning Models Can Have Multiple Channels. The Canonical Examples Are Images - Which Have Red - Green And Blue Color Channels. A Image Can Be Represented As A 3-Dimensional Tensor With The Dimensions Corresponding To Channel - Height - And Width. Natural Language Data Can Also Have Multiple Channels - In The Form Of Different Types Of Embeddings For ExampleSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At Hand

6. Batch Normalization

Maps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandA Technique That Normalizes Layer Inputs Per Mini-Batch. It Speed Up Training - Allows For The Usage Of Higher Learner Rates - And Can Act As A Regularizer. Batch Normalization Has Been Found To Be Very Effective For Convolutional And Feedforward Neural Networks But Hasn't Been Successfully Applied To Recurrent Neural NetworksSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

7. Glove

See Activation FunctionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandAn Unsupervised Learning Algorithm For Obtaining Vector Representations (Embeddings) For Words. Glove Vectors Serve The Same Purpose As Word2vec But Have Different Vector Representations Due To Being Trained On Co-Occurrence StatisticsAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

8. Highway Layer

Maps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandSee Activation FunctionA Type Of Neural Network Layer That Uses A Gating Mechanism To Control The Information Flow Through A Layer. Stacking Multiple Highway Layers Allows For Training Of Very Deep Networks. Highway Layers Work By Learning A Gating Function That Chooses Which Parts Of The Inputs To Pass Through And Which Parts To Pass Through A Transformation Function - Such As A Standard Affine Layer For Example. The Basic Formulation Of A Highway Layer Is T H(X) + (1 - T) X - Where T Is The Learned Gating Function With Values Between 0 And 1 - H(X) Is An Arbitrary Input Transformation And X Is The Input. Note That All Of These Must Have The Same SizeAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

9. Neural Machine Translation (Nmt)

Uses Neural Networks To Translate Between Languages - Such As English And French. Nmt Systems Can Be Trained End-To-End Using Bilingual Corpora - Which Differs From Traditional Machine Translation Systems That Require Hand-Crafted Features And Engineering. Nmt Systems Are Typically Implemented Using Encoder And Decoder Recurrent Neural Networks That Encode A Source Sentence And Produce A Target Sentence - RespectivelySee Activation FunctionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

10. Softmax

Typically Used To Convert A Vector Of Raw Scores Into Class Probabilities At The Output Layer Of A Neural Network Used For Classification. It Normalizes The Scores By Exponentiating And Dividing By A Normalization Constant. If We Are Dealing With A Large Number Of Classes - A Large Vocabulary In Machine Translation For Example - The Normalization Constant Is Expensive To Compute. There Exist Various Alternatives To Make The Computation More Efficient - Including Hierarchical Softmax Or Using A Sampling-Based Loss Such As NceSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At Hand

11. Average-Pooling

See Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasA Pooling Technique Used In Convolutional Neural Networks For Image Recognition. It Works By Sliding A Window Over Patches Of Features - Such As Pixels - And Taking The Average Of All Values Within The Window. It Compresses The Input Representation Into A Lower-Dimensional RepresentationMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At Hand

12. Exploding Gradient Problem

See Activation FunctionThe Opposite Of The Vanishing Gradient Problem. In Deep Neural Networks Gradients May Explode During Backpropagation - Resulting Number Overflows. A Common Technique To Deal With Exploding Gradients Is To Perform Gradient ClippingAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At Hand

13. Neural Turing Machine (Ntm)

An Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasNeural Network Architectures That Can Infer Simple Algorithms From Examples. For Example - A Ntm May Learn A Sorting Algorithm Through Example Inputs And Outputs. Ntms Typically Learn Some Form Of Memory And Attention Mechanism To Deal With State During Program ExecutionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandSee Activation Function

14. Ilsvrc

The Imagenet Large Scale Visual Recognition Challenge Evaluates Algorithms For Object Detection And Image Classification At Large Scale. It Is The Most Popular Academic Challenge In Computer Vision. Over The Past Years - Deep Learning Techniques Have Led To A Significant Reduction In Error Rates - From 30% To Less Than 5% - Beating Human Performance On Several Classification TasksMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasSee Activation Function

15. Gru

A Simplified Version Of An Lstm Unit With Fewer Parameters. Just Like An Lstm Cell - It Uses A Gating Mechanism To Allow Rnns To Efficiently Learn Long-Range Dependency By Preventing The Vanishing Gradient Problem. The Gru Consists Of A Reset And Update Gate That Determine Which Part Of The Old Memory To Keep Vs. Update With New Values At The Current Time StepMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

16. Negative Log Likelihood (Nll)

17. Mnist

See Activation FunctionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandPerhaps Most Commonly Used Image Recognition Dataset. It Consists Of 60 - 000 Training And 10 - 000 Test Examples Of Handwritten Digits. Each Image Is 28ã—28 Pixels Large. State Of The Art Models Typically Achieve Accuracies Of 99.5% Or Higher On The Test SetAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

18. Gradient Clipping

A Technique To Prevent Exploding Gradients In Very Deep Networks - Typically Recurrent Neural Networks. There Exist Various Ways To Perform Gradient Clipping - But The A Common One Is To Normalize The Gradients Of A Parameter Vector When Its L2 Norm Exceeds A Certain Threshold According To New_Gradients = Gradients * Threshold / L2_Norm(Gradients)Maps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

19. Restricted Boltzmann Machine (Rbn)

A Type Of Probabilistic Graphical Model That Can Be Interpreted As A Stochastic Artificial Neural Network. Rbns Learn A Representation Of The Data In An Unsupervised Manner. An Rbn Consists Of Visible And Hidden Layer - And Connections Between Binary Neurons In Each Of These Layers. Rbns Can Be Efficiently Trained Using Contrastive Divergence - An Approximation Of Gradient DescentMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

20. Adam

Maps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandAn Adaptive Learning Rate Algorithm Similar To Rmsprop - But Updates Are Directly Estimated Using A Running Average Of The First And Second Moment Of The Gradient And Also Include A Bias Correction TermSee Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

21. Multilayer Perceptron (Mlp(

See Activation FunctionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandA Feedforward Neural Network With Multiple Fully-Connected Layers That Use Nonlinear Activation Functions To Deal With Data Which Is Not Linearly Separable. An Mlp Is The Most Basic Form Of A Multilayer Neural Network - Or A Deep Neural Networks If It Has More Than 2 LayersAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or Keras

22. Icml

An Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasSee Activation FunctionMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandThe International Conference For Machine Learning - A Top-Tier Machine Learning Conference

23. Deep Dream

See Activation FunctionAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandA Technique Invented By Google That Tries To Distill The Knowledge Captured By A Deep Convolutional Neural Network. The Technique Can Generate New Images - Or Transform Existing Images And Give Them A Dreamlike Flavor - Especially When Applied Recursively

24. Alexnet

An Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasThe Name Of The Convolutional Neural Network Architecture That Won The Ilsvrc 2012 Competition By A Large Margin And Was Responsible For A Resurgence Of Interest In Cnns For Image Recognition. It Consists Of Five Convolutional Layers - Some Of Which Are Followed By Max-Pooling Layers - And Three Fully-Connected Layers With A Final 1000-Way Softmax. Alexnet Was Introduced In Imagenet Classification With Deep Convolutional Neural NetworksMaps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandSee Activation Function

25. Theano

Maps An Input Representation - Such As A Word Or Sentence - Into A Vector. A Popular Type Of Embedding Are Word Embeddings Such As Word2vec Or Glove. We Can Also Embed Sentences - Paragraphs Or Images. For Example - By Mapping Images And Their Textual Descriptions Into A Common Embedding Space And Minimizing The Distance Between Them - We Can Match Labels With Images. Embeddings Can Be Learned Explicitly - Such As In Word2vec - Or As Part Of A Supervised Task - Such As Sentiment Analysis. Often - The Input Layer Of A Network Is Initialized With Pre-Trained Embeddings - Which Are Then Fine-Tuned To The Task At HandAn Open Source C++/Python Software Library For Numerical Computation Using Data Flow Graphs - Particularly Deep Neural Networks. It Was Created By Google. In Terms Of Design - It Is Most Similar To Theano - And Lower-Level Than Caffe Or KerasSee Activation FunctionA Python Library That Allows You To Define - Optimize - And Evaluate Mathematical Expressions. It Contains Many Building Blocks For Deep Neural Networks. Theano Is A Low-Level Library Similar To Tensorflow. Higher-Level Libraries Include Keras And Caffe

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